I implemented DenseNet for a small data set that I have. In the reference implementation they used np_utils.to_categorical on CIFAR10 dataset to convert the labels to binary. I felt that with our OneHot encoding mechanism we achieved the same thing but would like to get expert opinion.

Its hard to tell since both return a 2 dimensional array with 1s and 0s.

I feel a little uncomfortable with these "connected to" layers especially if you run the same cells over and over again in your notebook it adds a new "connected to" each time. It may have unexpected side effects right now or in a future keras version. Therefore much better to use the copy_layer functions.

Two related issues I have come across:

In the lessons we append a sequential model to another. But then when you list the layers the sequential layer appears as one layer so you cannot directly see what is in it. I think better to use copy_layers again so you can view the resulting model.

If you save models and then read them back in; and then combine with layers from other models then sometimes you end up with layer name clashes. So you need functions to combine models that also forces unique layer names before compiling it.